1. Technical Field
The embodiments herein generally relate to categorization of multimedia content, and more particularly, to a system and method for categorizing explicit and non-explicit multimedia content.
2. Description of the Related Art
The World Wide Web (WWW) includes millions of multimedia content (e.g. videos, pictures etc.). According to a finding, about sixty percent of data consumed on the internet is multimedia content. In this age of information overload, it has become increasingly difficult for a user to locate multimedia content which is relevant. Users may find multimedia content which contain explicit content (e.g., multimedia content may contain excessive use of profane language, abusive language, or that is otherwise unsuitable for viewing by persons below a certain age), in a chance encounter or upon rigorously searching the web.
Typically, labeling and tagging are carried out for classification and for indicating an identity of online content. They may take the form of words, images, or other identifying marks. However, manual tagging is not feasible given an order of magnitude of data. Supervised learning is a machine learning based approach for inferring a function from labeled training data. The labeled training data has pairs consisting of an input feature vector (X) and a desired output value (Y). In the supervised learning based approach, each example is a pair consisting of an input object (typically a vector) and a desired output value. The inferred function should predict a correct output value for any valid input object. This requires a learning algorithm to generalize from the labeled training data to unseen situations in a “reasonable” way. This requires creating good training data, which takes a lot of time and manual effort. Accordingly, there remains a need for automatically categorizing one or more multimedia content as explicit or non-explicit multimedia content.